Physicians at the University Hospital Basel in Switzerland asked psychologist Mirjam Jenny of the Max Planck Institute for Human Development in Berlin to develop the diagnostic tool. The physicians had identified 88 signs of serious illness in 1,278 ER patients who had described nebulous problems.

From that data, computer learning programs sorted out 14 pieces of evidence that tagged many of those needing quick treatment while misclassifying few patients, Jenny reported June 11 at the Summer Institute on Bounded Rationality. She sifted those 14 illness predictors into a four-question decision tree that performed almost as well with the Basel data as the computer programs did.

The tree requires that physicians first determine whether a patient looks ill; then, whether the patient has signs of two or more medical ailments; next, whether the patient is at least 65 years old; and finally, whether the patient is male, putting him at higher risk of serious illness such as heart attack. A “yes” response to all questions sends up a red flag. A “no” to any question means a patient probably doesn’t need quick assistance.